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Identification of the Fracture Process in Gas Pipeline Steel Based on the Analysis of AE Signals

The paper presents the results of tests conducted to identify the damage process in specimens collected from the steel of a gas pipeline. The tests concerned specimens made of S235 steel subject to quasi-static loading—uniaxial tension until failure. Acoustic emission (AE) signals were recorded duri...

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Autores principales: Świt, Grzegorz, Dzioba, Ihor, Adamczak-Bugno, Anna, Krampikowska, Aleksandra
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9000342/
https://www.ncbi.nlm.nih.gov/pubmed/35407991
http://dx.doi.org/10.3390/ma15072659
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author Świt, Grzegorz
Dzioba, Ihor
Adamczak-Bugno, Anna
Krampikowska, Aleksandra
author_facet Świt, Grzegorz
Dzioba, Ihor
Adamczak-Bugno, Anna
Krampikowska, Aleksandra
author_sort Świt, Grzegorz
collection PubMed
description The paper presents the results of tests conducted to identify the damage process in specimens collected from the steel of a gas pipeline. The tests concerned specimens made of S235 steel subject to quasi-static loading—uniaxial tension until failure. Acoustic emission (AE) signals were recorded during the loading process along with force and elongation signals. Sections were collected from previously loaded specimens and subjected to microstructural examinations to determine the nature of material damage at different strain stages. The recorded AE signals were analyzed using the k-means clustering method, as well as time-frequency analysis. The results of metallographic tests and analysis of AE signals identified frequency spectra characteristic of different stages of the process of material damage.
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spelling pubmed-90003422022-04-12 Identification of the Fracture Process in Gas Pipeline Steel Based on the Analysis of AE Signals Świt, Grzegorz Dzioba, Ihor Adamczak-Bugno, Anna Krampikowska, Aleksandra Materials (Basel) Article The paper presents the results of tests conducted to identify the damage process in specimens collected from the steel of a gas pipeline. The tests concerned specimens made of S235 steel subject to quasi-static loading—uniaxial tension until failure. Acoustic emission (AE) signals were recorded during the loading process along with force and elongation signals. Sections were collected from previously loaded specimens and subjected to microstructural examinations to determine the nature of material damage at different strain stages. The recorded AE signals were analyzed using the k-means clustering method, as well as time-frequency analysis. The results of metallographic tests and analysis of AE signals identified frequency spectra characteristic of different stages of the process of material damage. MDPI 2022-04-04 /pmc/articles/PMC9000342/ /pubmed/35407991 http://dx.doi.org/10.3390/ma15072659 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Świt, Grzegorz
Dzioba, Ihor
Adamczak-Bugno, Anna
Krampikowska, Aleksandra
Identification of the Fracture Process in Gas Pipeline Steel Based on the Analysis of AE Signals
title Identification of the Fracture Process in Gas Pipeline Steel Based on the Analysis of AE Signals
title_full Identification of the Fracture Process in Gas Pipeline Steel Based on the Analysis of AE Signals
title_fullStr Identification of the Fracture Process in Gas Pipeline Steel Based on the Analysis of AE Signals
title_full_unstemmed Identification of the Fracture Process in Gas Pipeline Steel Based on the Analysis of AE Signals
title_short Identification of the Fracture Process in Gas Pipeline Steel Based on the Analysis of AE Signals
title_sort identification of the fracture process in gas pipeline steel based on the analysis of ae signals
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9000342/
https://www.ncbi.nlm.nih.gov/pubmed/35407991
http://dx.doi.org/10.3390/ma15072659
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